Modern machine learning capabilities are rapidly changing and improving how some of the most complex and data-intensive computing problems are solved. A performance of a machine learning model is governed mainly in the manner(s) in which the machine learning model is trained and based on the hyperparameters of the machine learning model set prior to the training of the model. As referenced in passing, the hyperparameters of the machine learning models are parameters whose values are set prior to the commencement of the machine learning process rather than derived by the machine learning model during training. Examples include the number of trees in a random forest or the number of hidden layers in a deep neural network. Adjusting the values of the hyperparameters of a machine learning model by any amount typically results in a large impact on a performance of the machine learning model and correspondingly, a computational performance of a computer implementing the machine learning model.
However, many existing machine learning models are not implemented with optimal hyperparameters well-suited for achieving the best predictive performances and/or classification performances. Rather, the many existing machine learning models are implemented with default hyperparameters that have not been tuned or optimized for a specific computing problem for which the machine learning models are being used.
Thus, there is a need in the machine learning and computing field to create an improved optimization platform to test and improve machine learning models (e.g., in-product machine learning models). The embodiments of the present application described herein provide technical solutions that address, at least, the need described above, as well as the technical deficiencies of the state of the art described throughout the present application.